The location of edges in an image is fundamental to several types of image processing. In pattern recognition, image edges define boundaries between image segments. Either these image segments or the defined boundaries are correlated with reference images in order to recognize objects in the image. Thus, the location of edges in an image is an important factor in pattern recognition.
Edges are also important for systems attempting to model human vision. The human eye is sensitive to edges and perceives a low-resolution image having hard edges as being more subjectively pleasing than an image of similar resolution which has soft edges. Thus, much of the information value of an image is in its edges.
Furthermore, image edges are important for image compression systems. Relatively efficient intra-frame video encoding systems exist which are based on linear transforms, the JPEG compression system, for example, is based on discrete cosine transforms. Linear transforms have difficulty compressing sharp edges. Consequently, these edges tend to blur and soften through multiple compression and decompression operations. Thus, systems based on linear transforms are not well suited for multi-generational compression and decompression. An image compression system which maintains edges in the image can produce an encoded image which is more subjectively pleasing through several compression/decompression cycles. An edge finder would be beneficial in any compression system which maintains edges
Many edge finders currently in use locate edges by calculating gradients of pixel values, vertically, horizontally, or both, in the image. It is well recognized, however, that edges are discontinuities in the image where the measurement of gradients can be ill-defined. This behavior of gradient-based edge finders is sensitive to noise. Another type of edge finder operates by identifying and growing regions in the image. At the end of the region growing process, the boundaries of the regions correspond to edges in the original image. For this type of edge finder, a starting point may be to find locations in the image where edges do not exist.